The Research Showcase will be starting in about 30 minutes! Info below: On Thu, Feb 14, 2019 at 11:20 AM Janna Layton <jlay...@wikimedia.org> wrote:
> Hello everyone, > > The next Research Showcase, “The_Tower_of_Babel.jpg” and “A Warm Welcome, > Not a Cold Start,” will be live-streamed next Wednesday, February 20, 2019, > at 11:30 AM PST/19:30 UTC. The first presentation is about how images are > used across language editions, and the second is about new editors. > > > YouTube stream: https://www.youtube.com/watch?v=_jpJIFXwlEg > > As usual, you can join the conversation on IRC at #wikimedia-research. You > can also watch our past research showcases here: > https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase > > This month's presentations: > > The_Tower_of_Babel.jpg: Diversity of Visual Encyclopedic Knowledge Across > Wikipedia Language Editions > > > By Shiqing He (presenting, University of Michigan), Brent Hecht > (presenting, Northwestern University), Allen Yilun Lin (Northwestern > University), Eytan Adar (University of Michigan), ICWSM'18. > > > Across all Wikipedia language editions, millions of images augment text in > critical ways. This visual encyclopedic knowledge is an important form of > wikiwork for editors, a critical part of reader experience, an emerging > resource for machine learning, and a lens into cultural differences. > However, Wikipedia research--and cross-language edition Wikipedia research > in particular--has thus far been limited to text. In this paper, we assess > the diversity of visual encyclopedic knowledge across 25 language editions > and compare our findings to those reported for textual content. Unlike > text, translation in images is largely unnecessary. Additionally, the > Wikimedia Foundation, through the Wikipedia Commons, has taken steps to > simplify cross-language image sharing. While we may expect that these > factors would reduce image diversity, we find that cross-language image > diversity rivals, and often exceeds, that found in text. We find that > diversity varies between language pairs and content types, but that many > images are unique to different language editions. Our findings have > implications for readers (in what imagery they see), for editors (in > deciding what images to use), for researchers (who study cultural > variations), and for machine learning developers (who use Wikipedia for > training models). > > > A Warm Welcome, Not a Cold Start: Eliciting New Editors' Interests via > Questionnaires > > > By Ramtin Yazdanian (presenting, Ecole Polytechnique Federale de Lausanne) > > Every day, thousands of users sign up as new Wikipedia contributors. Once > joined, these users have to decide which articles to contribute to, which > users to reach out to and learn from or collaborate with, etc. Any such > task is a hard and potentially frustrating one given the sheer size of > Wikipedia. Supporting newcomers in their first steps by recommending > articles they would enjoy editing or editors they would enjoy collaborating > with is thus a promising route toward converting them into long-term > contributors. Standard recommender systems, however, rely on users' > histories of previous interactions with the platform. As such, these > systems cannot make high-quality recommendations to newcomers without any > previous interactions -- the so-called cold-start problem. Our aim is to > address the cold-start problem on Wikipedia by developing a method for > automatically building short questionnaires that, when completed by a newly > registered Wikipedia user, can be used for a variety of purposes, including > article recommendations that can help new editors get started. Our > questionnaires are constructed based on the text of Wikipedia articles as > well as the history of contributions by the already onboarded Wikipedia > editors. We have assessed the quality of our questionnaire-based > recommendations in an offline evaluation using historical data, as well as > an online evaluation with hundreds of real Wikipedia newcomers, concluding > that our method provides cohesive, human-readable questions that perform > well against several baselines. By addressing the cold-start problem, this > work can help with the sustainable growth and maintenance of Wikipedia's > diverse editor community. > > > -- > Janna Layton (she, her) > Administrative Assistant - Audiences & Technology > Wikimedia Foundation <https://wikimediafoundation.org/> > -- Janna Layton (she, her) Administrative Assistant - Audiences & Technology Wikimedia Foundation <https://wikimediafoundation.org/> _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines and https://meta.wikimedia.org/wiki/Wikimedia-l New messages to: Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, <mailto:wikimedia-l-requ...@lists.wikimedia.org?subject=unsubscribe>
